Estudiante Ing. Forestal
NASA
“Desde 1880, el nivel del mar global ha aumentado 20 centimetros. Para el 2100 se proyecta que aumente entre 30 y 122 centimetros más.”
Fuentes
pacman::p_load(TSstudio, tsbox, tsoutliers, tidyverse,
lubridate, xts, magrittr, forecast, dygraphs)
url <- 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt'
cov <- read.table(file = url) %>%
select(3, 4) %>%
rename(fecha = 1, co2 = 2) %>%
mutate(fecha = date_decimal(fecha) %>%
floor_date(unit = 'month')) %>%
filter(fecha <= '2013-12-01')
data <- read.csv('gmsl.csv') %>%
mutate(fecha = date_decimal(Time) %>%
floor_date(unit = 'month')) %>%
rename(gsml = 2) %>%
select(4, 2)%>%
filter(fecha > '1958-02-01') %>%
left_join(y = cov) %>%
{xts(x = .[,2:3], order.by = .$fecha)}
rm(cov)
dygraph(data, main = 'Nivel medio del mar global vs
Concentración de CO2 en la atmósfera') %>%
dySeries("co2", axis = 'y2', label = 'CO2') %>%
dySeries('gsml', label = 'GSML') %>%
dyAxis('y', label = 'Nivel del mar (mm)') %>%
dyAxis('y2', label = 'CO2 (ppm)', independentTicks = T) %>%
dyRangeSelector()
z test of coefficients:
Estimate Std. Error z value Pr(>|z|)
ar1 0.797070 0.054991 14.4947 < 2.2e-16 ***
ar2 -0.095533 0.054919 -1.7395 0.08194 .
ma1 -0.339090 0.040237 -8.4274 < 2.2e-16 ***
ma2 0.208116 0.038717 5.3753 7.646e-08 ***
ma3 -0.693544 0.028246 -24.5535 < 2.2e-16 ***
drift 0.208518 0.041471 5.0281 4.955e-07 ***
co2 -0.171228 0.087716 -1.9521 0.05093 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
\[ \begin{alignat}{2} &\text{let}\quad &&y_{t} = \operatorname{y}_{\operatorname{0}} +0.21\operatorname{t} -0.17\operatorname{co2}_{\operatorname{t}} +\eta_{t} \\ &\text{where}\quad &&(1 -0.8\operatorname{B} +0.1\operatorname{B}^{\operatorname{2}} )\ (1 - \operatorname{B}) \eta_{t} \\ & &&= (1 -0.34\operatorname{B} +0.21\operatorname{B}^{\operatorname{2}} -0.69\operatorname{B}^{\operatorname{3}} )\ \varepsilon_{t} \\ &\text{where}\quad &&\varepsilon_{t} \sim{WN(0, \sigma^{2})} \end{alignat} \]
Series:
Regression with ARIMA(1,1,3) errors
Coefficients:
ar1 ma1 ma2 ma3 xreg
0.7152 -0.2650 0.1905 -0.6741 -0.1222
s.e. 0.0439 0.0387 0.0333 0.0275 0.0851
sigma^2 = 3.074: log likelihood = -1299.53
AIC=2611.06 AICc=2611.19 BIC=2637.99
No outliers were detected.
prediccion <- forecast(object = modelo, h = 12, level = 95, xreg = test[,'co2'])
serie <- prediccion %>%
as_tibble() %>%
ts(start = c(2013, 1), end = c(2013, 12), frequency = 12) %>%
{cbind(data[, 'gsml'], .)}
colnames(serie) <- c('gsml', 'predicho', 'lwr', 'upr')
serie %>%
dygraph() %>%
dySeries('gsml', label = 'Observada') %>%
dySeries(c('lwr', 'predicho', 'upr'), label = 'Predicho') %>%
dyRangeSelector()Series de Tiempo Univaridas